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<a href="#pub-types">Public Types</a> &#124;
<a href="#pub-methods">Public Member Functions</a> &#124;
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<div class="title">imageNet Class Reference<div class="ingroups"><a class="el" href="group__deepVision.html">DNN Vision Library (jetson-inference)</a> &raquo; <a class="el" href="group__imageNet.html">imageNet</a></div></div>  </div>
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<p>Image recognition with classification networks, using TensorRT.  
 <a href="classimageNet.html#details">More...</a></p>

<p><code>#include &lt;<a class="el" href="imageNet_8h_source.html">imageNet.h</a>&gt;</code></p>
<div class="dynheader">
Inheritance diagram for imageNet:</div>
<div class="dyncontent">
 <div class="center">
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<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-types"></a>
Public Types</h2></td></tr>
<tr class="memitem:aae203e533ecceb314857f99a2817fc81"><td class="memItemLeft" align="right" valign="top">typedef std::vector&lt; std::pair&lt; uint32_t, float &gt; &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classimageNet.html#aae203e533ecceb314857f99a2817fc81">Classifications</a></td></tr>
<tr class="memdesc:aae203e533ecceb314857f99a2817fc81"><td class="mdescLeft">&#160;</td><td class="mdescRight">List of classification results where each entry represents a (classID, confidence) pair.  <a href="classimageNet.html#aae203e533ecceb314857f99a2817fc81">More...</a><br /></td></tr>
<tr class="separator:aae203e533ecceb314857f99a2817fc81"><td class="memSeparator" colspan="2">&#160;</td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a>
Public Member Functions</h2></td></tr>
<tr class="memitem:af6bd86e81ff9e67ffe19b575c17ed104"><td class="memItemLeft" align="right" valign="top">virtual&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classimageNet.html#af6bd86e81ff9e67ffe19b575c17ed104">~imageNet</a> ()</td></tr>
<tr class="memdesc:af6bd86e81ff9e67ffe19b575c17ed104"><td class="mdescLeft">&#160;</td><td class="mdescRight">Destroy.  <a href="classimageNet.html#af6bd86e81ff9e67ffe19b575c17ed104">More...</a><br /></td></tr>
<tr class="separator:af6bd86e81ff9e67ffe19b575c17ed104"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a0023fb5bcd45dcefdac6a7df4bf9bab5"><td class="memTemplParams" colspan="2">template&lt;typename T &gt; </td></tr>
<tr class="memitem:a0023fb5bcd45dcefdac6a7df4bf9bab5"><td class="memTemplItemLeft" align="right" valign="top">int&#160;</td><td class="memTemplItemRight" valign="bottom"><a class="el" href="classimageNet.html#a0023fb5bcd45dcefdac6a7df4bf9bab5">Classify</a> (T *image, uint32_t width, uint32_t height, float *confidence=NULL)</td></tr>
<tr class="memdesc:a0023fb5bcd45dcefdac6a7df4bf9bab5"><td class="mdescLeft">&#160;</td><td class="mdescRight">Predict the maximum-likelihood image class whose confidence meets the minimum threshold.  <a href="classimageNet.html#a0023fb5bcd45dcefdac6a7df4bf9bab5">More...</a><br /></td></tr>
<tr class="separator:a0023fb5bcd45dcefdac6a7df4bf9bab5"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:adfaf6892bc745af042089ee995b33745"><td class="memItemLeft" align="right" valign="top">int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classimageNet.html#adfaf6892bc745af042089ee995b33745">Classify</a> (void *image, uint32_t width, uint32_t height, <a class="el" href="group__imageFormat.html#ga931c48e08f361637d093355d64583406">imageFormat</a> format, float *confidence=NULL)</td></tr>
<tr class="memdesc:adfaf6892bc745af042089ee995b33745"><td class="mdescLeft">&#160;</td><td class="mdescRight">Predict the maximum-likelihood image class whose confidence meets the minimum threshold.  <a href="classimageNet.html#adfaf6892bc745af042089ee995b33745">More...</a><br /></td></tr>
<tr class="separator:adfaf6892bc745af042089ee995b33745"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a1c645a989562b8bc8f1137f0f43fc6b7"><td class="memItemLeft" align="right" valign="top">int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classimageNet.html#a1c645a989562b8bc8f1137f0f43fc6b7">Classify</a> (float *rgba, uint32_t width, uint32_t height, float *confidence=NULL, <a class="el" href="group__imageFormat.html#ga931c48e08f361637d093355d64583406">imageFormat</a> format=<a class="el" href="group__imageFormat.html#gga931c48e08f361637d093355d64583406a9396c3fdae6987bbf4abc2b2e63e3815">IMAGE_RGBA32F</a>)</td></tr>
<tr class="memdesc:a1c645a989562b8bc8f1137f0f43fc6b7"><td class="mdescLeft">&#160;</td><td class="mdescRight">Predict the maximum-likelihood image class whose confidence meets the minimum threshold.  <a href="classimageNet.html#a1c645a989562b8bc8f1137f0f43fc6b7">More...</a><br /></td></tr>
<tr class="separator:a1c645a989562b8bc8f1137f0f43fc6b7"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ad39000debfd9ac6352f85b93ae27b305"><td class="memTemplParams" colspan="2">template&lt;typename T &gt; </td></tr>
<tr class="memitem:ad39000debfd9ac6352f85b93ae27b305"><td class="memTemplItemLeft" align="right" valign="top">int&#160;</td><td class="memTemplItemRight" valign="bottom"><a class="el" href="classimageNet.html#ad39000debfd9ac6352f85b93ae27b305">Classify</a> (T *image, uint32_t width, uint32_t height, <a class="el" href="classimageNet.html#aae203e533ecceb314857f99a2817fc81">Classifications</a> &amp;classifications, int topK=1)</td></tr>
<tr class="memdesc:ad39000debfd9ac6352f85b93ae27b305"><td class="mdescLeft">&#160;</td><td class="mdescRight">Classify the image and return the topK image classification results that meet the minimum confidence threshold set by <a class="el" href="classimageNet.html#a6594a1190c5d515c1987aefcee5d819f" title="Set the confidence threshold used for classification.">SetThreshold()</a> or the <code>--threshold</code> command-line argument.  <a href="classimageNet.html#ad39000debfd9ac6352f85b93ae27b305">More...</a><br /></td></tr>
<tr class="separator:ad39000debfd9ac6352f85b93ae27b305"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a364792017fbc14e0464f72f4e2bc1267"><td class="memItemLeft" align="right" valign="top">int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classimageNet.html#a364792017fbc14e0464f72f4e2bc1267">Classify</a> (void *image, uint32_t width, uint32_t height, <a class="el" href="group__imageFormat.html#ga931c48e08f361637d093355d64583406">imageFormat</a> format, <a class="el" href="classimageNet.html#aae203e533ecceb314857f99a2817fc81">Classifications</a> &amp;classifications, int topK=1)</td></tr>
<tr class="memdesc:a364792017fbc14e0464f72f4e2bc1267"><td class="mdescLeft">&#160;</td><td class="mdescRight">Classify the image and return the topK image classification results that meet the minimum confidence threshold set by <a class="el" href="classimageNet.html#a6594a1190c5d515c1987aefcee5d819f" title="Set the confidence threshold used for classification.">SetThreshold()</a> or the <code>--threshold</code> command-line argument.  <a href="classimageNet.html#a364792017fbc14e0464f72f4e2bc1267">More...</a><br /></td></tr>
<tr class="separator:a364792017fbc14e0464f72f4e2bc1267"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a478f25126524a256e81ec264aad7e27a"><td class="memItemLeft" align="right" valign="top">uint32_t&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classimageNet.html#a478f25126524a256e81ec264aad7e27a">GetNumClasses</a> () const</td></tr>
<tr class="memdesc:a478f25126524a256e81ec264aad7e27a"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the number of image recognition classes (typically 1000)  <a href="classimageNet.html#a478f25126524a256e81ec264aad7e27a">More...</a><br /></td></tr>
<tr class="separator:a478f25126524a256e81ec264aad7e27a"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a3b41ed0e039638353e6964ada588becb"><td class="memItemLeft" align="right" valign="top">const char *&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classimageNet.html#a3b41ed0e039638353e6964ada588becb">GetClassLabel</a> (int index) const</td></tr>
<tr class="memdesc:a3b41ed0e039638353e6964ada588becb"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the description of a particular class.  <a href="classimageNet.html#a3b41ed0e039638353e6964ada588becb">More...</a><br /></td></tr>
<tr class="separator:a3b41ed0e039638353e6964ada588becb"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a673728c04ae909cb3068c2a1ace1e5a7"><td class="memItemLeft" align="right" valign="top">const char *&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classimageNet.html#a673728c04ae909cb3068c2a1ace1e5a7">GetClassDesc</a> (int index) const</td></tr>
<tr class="memdesc:a673728c04ae909cb3068c2a1ace1e5a7"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the description of a particular class.  <a href="classimageNet.html#a673728c04ae909cb3068c2a1ace1e5a7">More...</a><br /></td></tr>
<tr class="separator:a673728c04ae909cb3068c2a1ace1e5a7"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ab8ee0abaa0ebf38becfc0cbaf6956712"><td class="memItemLeft" align="right" valign="top">const char *&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classimageNet.html#ab8ee0abaa0ebf38becfc0cbaf6956712">GetClassSynset</a> (int index) const</td></tr>
<tr class="memdesc:ab8ee0abaa0ebf38becfc0cbaf6956712"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the class synset category of a particular class.  <a href="classimageNet.html#ab8ee0abaa0ebf38becfc0cbaf6956712">More...</a><br /></td></tr>
<tr class="separator:ab8ee0abaa0ebf38becfc0cbaf6956712"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a04276f915b0f40d6257cbed3fe47dc5f"><td class="memItemLeft" align="right" valign="top">const char *&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classimageNet.html#a04276f915b0f40d6257cbed3fe47dc5f">GetClassPath</a> () const</td></tr>
<tr class="memdesc:a04276f915b0f40d6257cbed3fe47dc5f"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the path to the file containing the class descriptions.  <a href="classimageNet.html#a04276f915b0f40d6257cbed3fe47dc5f">More...</a><br /></td></tr>
<tr class="separator:a04276f915b0f40d6257cbed3fe47dc5f"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ac074f4e67f639c89238a3dcb6771c4cd"><td class="memItemLeft" align="right" valign="top">float&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classimageNet.html#ac074f4e67f639c89238a3dcb6771c4cd">GetThreshold</a> () const</td></tr>
<tr class="memdesc:ac074f4e67f639c89238a3dcb6771c4cd"><td class="mdescLeft">&#160;</td><td class="mdescRight">Return the confidence threshold used for classification.  <a href="classimageNet.html#ac074f4e67f639c89238a3dcb6771c4cd">More...</a><br /></td></tr>
<tr class="separator:ac074f4e67f639c89238a3dcb6771c4cd"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a6594a1190c5d515c1987aefcee5d819f"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classimageNet.html#a6594a1190c5d515c1987aefcee5d819f">SetThreshold</a> (float threshold)</td></tr>
<tr class="memdesc:a6594a1190c5d515c1987aefcee5d819f"><td class="mdescLeft">&#160;</td><td class="mdescRight">Set the confidence threshold used for classification.  <a href="classimageNet.html#a6594a1190c5d515c1987aefcee5d819f">More...</a><br /></td></tr>
<tr class="separator:a6594a1190c5d515c1987aefcee5d819f"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a37b850912da4c60f93c7798d13baf777"><td class="memItemLeft" align="right" valign="top">float&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classimageNet.html#a37b850912da4c60f93c7798d13baf777">GetSmoothing</a> () const</td></tr>
<tr class="memdesc:a37b850912da4c60f93c7798d13baf777"><td class="mdescLeft">&#160;</td><td class="mdescRight">Return the temporal smoothing weight or number of frames in the smoothing window.  <a href="classimageNet.html#a37b850912da4c60f93c7798d13baf777">More...</a><br /></td></tr>
<tr class="separator:a37b850912da4c60f93c7798d13baf777"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:adc93ae6107469e3898872917f6736d85"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classimageNet.html#adc93ae6107469e3898872917f6736d85">SetSmoothing</a> (float factor)</td></tr>
<tr class="memdesc:adc93ae6107469e3898872917f6736d85"><td class="mdescLeft">&#160;</td><td class="mdescRight">Enable temporal smoothing of the results using EWMA (exponentially-weighted moving average).  <a href="classimageNet.html#adc93ae6107469e3898872917f6736d85">More...</a><br /></td></tr>
<tr class="separator:adc93ae6107469e3898872917f6736d85"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="inherit_header pub_methods_classtensorNet"><td colspan="2" onclick="javascript:toggleInherit('pub_methods_classtensorNet')"><img src="closed.png" alt="-"/>&#160;Public Member Functions inherited from <a class="el" href="classtensorNet.html">tensorNet</a></td></tr>
<tr class="memitem:ad19aafbfa262f9b8ffb0bff561f4d7f7 inherit pub_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">virtual&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#ad19aafbfa262f9b8ffb0bff561f4d7f7">~tensorNet</a> ()</td></tr>
<tr class="memdesc:ad19aafbfa262f9b8ffb0bff561f4d7f7 inherit pub_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Destory.  <a href="classtensorNet.html#ad19aafbfa262f9b8ffb0bff561f4d7f7">More...</a><br /></td></tr>
<tr class="separator:ad19aafbfa262f9b8ffb0bff561f4d7f7 inherit pub_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a2e63d4670461814bd863ee0d9bd41526 inherit pub_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#a2e63d4670461814bd863ee0d9bd41526">LoadNetwork</a> (const char *prototxt, const char *model, const char *mean=NULL, const char *input_blob=&quot;data&quot;, const char *output_blob=&quot;prob&quot;, uint32_t maxBatchSize=<a class="el" href="group__tensorNet.html#ga5a46a965749d6118e01307fd4d4865c9">DEFAULT_MAX_BATCH_SIZE</a>, <a class="el" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a> precision=<a class="el" href="group__tensorNet.html#ggaac6604fd52c6e5db82877390e0378623a1d325738f49e8e4c424ff671624e66f9">TYPE_FASTEST</a>, <a class="el" href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a> device=<a class="el" href="group__tensorNet.html#ggaa5d3f9981cdbd91516c1474006a80fe4adc7f3f88455afa81458863e5b3092e4b">DEVICE_GPU</a>, bool allowGPUFallback=true, nvinfer1::IInt8Calibrator *calibrator=NULL, cudaStream_t stream=NULL)</td></tr>
<tr class="memdesc:a2e63d4670461814bd863ee0d9bd41526 inherit pub_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Load a new network instance.  <a href="classtensorNet.html#a2e63d4670461814bd863ee0d9bd41526">More...</a><br /></td></tr>
<tr class="separator:a2e63d4670461814bd863ee0d9bd41526 inherit pub_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a0a06ffd12b465f39160f4a6925cccd9f inherit pub_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#a0a06ffd12b465f39160f4a6925cccd9f">LoadNetwork</a> (const char *prototxt, const char *model, const char *mean, const char *input_blob, const std::vector&lt; std::string &gt; &amp;output_blobs, uint32_t maxBatchSize=<a class="el" href="group__tensorNet.html#ga5a46a965749d6118e01307fd4d4865c9">DEFAULT_MAX_BATCH_SIZE</a>, <a class="el" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a> precision=<a class="el" href="group__tensorNet.html#ggaac6604fd52c6e5db82877390e0378623a1d325738f49e8e4c424ff671624e66f9">TYPE_FASTEST</a>, <a class="el" href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a> device=<a class="el" href="group__tensorNet.html#ggaa5d3f9981cdbd91516c1474006a80fe4adc7f3f88455afa81458863e5b3092e4b">DEVICE_GPU</a>, bool allowGPUFallback=true, nvinfer1::IInt8Calibrator *calibrator=NULL, cudaStream_t stream=NULL)</td></tr>
<tr class="memdesc:a0a06ffd12b465f39160f4a6925cccd9f inherit pub_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Load a new network instance with multiple output layers.  <a href="classtensorNet.html#a0a06ffd12b465f39160f4a6925cccd9f">More...</a><br /></td></tr>
<tr class="separator:a0a06ffd12b465f39160f4a6925cccd9f inherit pub_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a68a6f21680ae91bc51bea376221d1c48 inherit pub_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#a68a6f21680ae91bc51bea376221d1c48">LoadNetwork</a> (const char *prototxt, const char *model, const char *mean, const std::vector&lt; std::string &gt; &amp;input_blobs, const std::vector&lt; std::string &gt; &amp;output_blobs, uint32_t maxBatchSize=<a class="el" href="group__tensorNet.html#ga5a46a965749d6118e01307fd4d4865c9">DEFAULT_MAX_BATCH_SIZE</a>, <a class="el" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a> precision=<a class="el" href="group__tensorNet.html#ggaac6604fd52c6e5db82877390e0378623a1d325738f49e8e4c424ff671624e66f9">TYPE_FASTEST</a>, <a class="el" href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a> device=<a class="el" href="group__tensorNet.html#ggaa5d3f9981cdbd91516c1474006a80fe4adc7f3f88455afa81458863e5b3092e4b">DEVICE_GPU</a>, bool allowGPUFallback=true, nvinfer1::IInt8Calibrator *calibrator=NULL, cudaStream_t stream=NULL)</td></tr>
<tr class="memdesc:a68a6f21680ae91bc51bea376221d1c48 inherit pub_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Load a new network instance with multiple input layers.  <a href="classtensorNet.html#a68a6f21680ae91bc51bea376221d1c48">More...</a><br /></td></tr>
<tr class="separator:a68a6f21680ae91bc51bea376221d1c48 inherit pub_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a168c7f75c9fd6d264afd016e144f3878 inherit pub_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#a168c7f75c9fd6d264afd016e144f3878">LoadNetwork</a> (const char *prototxt, const char *model, const char *mean, const char *input_blob, const <a class="el" href="tensorNet_8h.html#a64c8f3dfeacfa962ff9e23c586aedd1b">Dims3</a> &amp;input_dims, const std::vector&lt; std::string &gt; &amp;output_blobs, uint32_t maxBatchSize=<a class="el" href="group__tensorNet.html#ga5a46a965749d6118e01307fd4d4865c9">DEFAULT_MAX_BATCH_SIZE</a>, <a class="el" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a> precision=<a class="el" href="group__tensorNet.html#ggaac6604fd52c6e5db82877390e0378623a1d325738f49e8e4c424ff671624e66f9">TYPE_FASTEST</a>, <a class="el" href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a> device=<a class="el" href="group__tensorNet.html#ggaa5d3f9981cdbd91516c1474006a80fe4adc7f3f88455afa81458863e5b3092e4b">DEVICE_GPU</a>, bool allowGPUFallback=true, nvinfer1::IInt8Calibrator *calibrator=NULL, cudaStream_t stream=NULL)</td></tr>
<tr class="memdesc:a168c7f75c9fd6d264afd016e144f3878 inherit pub_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Load a new network instance (this variant is used for UFF models)  <a href="classtensorNet.html#a168c7f75c9fd6d264afd016e144f3878">More...</a><br /></td></tr>
<tr class="separator:a168c7f75c9fd6d264afd016e144f3878 inherit pub_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a8f34a6001c2da01662b85670de9246e4 inherit pub_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#a8f34a6001c2da01662b85670de9246e4">LoadNetwork</a> (const char *prototxt, const char *model, const char *mean, const std::vector&lt; std::string &gt; &amp;input_blobs, const std::vector&lt; <a class="el" href="tensorNet_8h.html#a64c8f3dfeacfa962ff9e23c586aedd1b">Dims3</a> &gt; &amp;input_dims, const std::vector&lt; std::string &gt; &amp;output_blobs, uint32_t maxBatchSize=<a class="el" href="group__tensorNet.html#ga5a46a965749d6118e01307fd4d4865c9">DEFAULT_MAX_BATCH_SIZE</a>, <a class="el" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a> precision=<a class="el" href="group__tensorNet.html#ggaac6604fd52c6e5db82877390e0378623a1d325738f49e8e4c424ff671624e66f9">TYPE_FASTEST</a>, <a class="el" href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a> device=<a class="el" href="group__tensorNet.html#ggaa5d3f9981cdbd91516c1474006a80fe4adc7f3f88455afa81458863e5b3092e4b">DEVICE_GPU</a>, bool allowGPUFallback=true, nvinfer1::IInt8Calibrator *calibrator=NULL, cudaStream_t stream=NULL)</td></tr>
<tr class="memdesc:a8f34a6001c2da01662b85670de9246e4 inherit pub_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Load a new network instance with multiple input layers (used for UFF models)  <a href="classtensorNet.html#a8f34a6001c2da01662b85670de9246e4">More...</a><br /></td></tr>
<tr class="separator:a8f34a6001c2da01662b85670de9246e4 inherit pub_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:acb8076f6ab8d13b6507140826cf438d8 inherit pub_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#acb8076f6ab8d13b6507140826cf438d8">LoadEngine</a> (const char *engine_filename, const std::vector&lt; std::string &gt; &amp;input_blobs, const std::vector&lt; std::string &gt; &amp;output_blobs, nvinfer1::IPluginFactory *pluginFactory=NULL, <a class="el" href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a> device=<a class="el" href="group__tensorNet.html#ggaa5d3f9981cdbd91516c1474006a80fe4adc7f3f88455afa81458863e5b3092e4b">DEVICE_GPU</a>, cudaStream_t stream=NULL)</td></tr>
<tr class="memdesc:acb8076f6ab8d13b6507140826cf438d8 inherit pub_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Load a network instance from a serialized engine plan file.  <a href="classtensorNet.html#acb8076f6ab8d13b6507140826cf438d8">More...</a><br /></td></tr>
<tr class="separator:acb8076f6ab8d13b6507140826cf438d8 inherit pub_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:aaa4efe2b8d91fe914a22c87b725ac063 inherit pub_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#aaa4efe2b8d91fe914a22c87b725ac063">LoadEngine</a> (char *engine_stream, size_t engine_size, const std::vector&lt; std::string &gt; &amp;input_blobs, const std::vector&lt; std::string &gt; &amp;output_blobs, nvinfer1::IPluginFactory *pluginFactory=NULL, <a class="el" href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a> device=<a class="el" href="group__tensorNet.html#ggaa5d3f9981cdbd91516c1474006a80fe4adc7f3f88455afa81458863e5b3092e4b">DEVICE_GPU</a>, cudaStream_t stream=NULL)</td></tr>
<tr class="memdesc:aaa4efe2b8d91fe914a22c87b725ac063 inherit pub_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Load a network instance from a serialized engine plan file.  <a href="classtensorNet.html#aaa4efe2b8d91fe914a22c87b725ac063">More...</a><br /></td></tr>
<tr class="separator:aaa4efe2b8d91fe914a22c87b725ac063 inherit pub_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a2d6fe13696a49d61e9abfa9729153e65 inherit pub_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#a2d6fe13696a49d61e9abfa9729153e65">LoadEngine</a> (nvinfer1::ICudaEngine *engine, const std::vector&lt; std::string &gt; &amp;input_blobs, const std::vector&lt; std::string &gt; &amp;output_blobs, <a class="el" href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a> device=<a class="el" href="group__tensorNet.html#ggaa5d3f9981cdbd91516c1474006a80fe4adc7f3f88455afa81458863e5b3092e4b">DEVICE_GPU</a>, cudaStream_t stream=NULL)</td></tr>
<tr class="memdesc:a2d6fe13696a49d61e9abfa9729153e65 inherit pub_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Load network resources from an existing TensorRT engine instance.  <a href="classtensorNet.html#a2d6fe13696a49d61e9abfa9729153e65">More...</a><br /></td></tr>
<tr class="separator:a2d6fe13696a49d61e9abfa9729153e65 inherit pub_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a89755f8e4b72ead7460deed394967386 inherit pub_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#a89755f8e4b72ead7460deed394967386">LoadEngine</a> (const char *filename, char **stream, size_t *size)</td></tr>
<tr class="memdesc:a89755f8e4b72ead7460deed394967386 inherit pub_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Load a serialized engine plan file into memory.  <a href="classtensorNet.html#a89755f8e4b72ead7460deed394967386">More...</a><br /></td></tr>
<tr class="separator:a89755f8e4b72ead7460deed394967386 inherit pub_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a3413eb0ad4f240f457f192f39e2e03e8 inherit pub_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#a3413eb0ad4f240f457f192f39e2e03e8">EnableLayerProfiler</a> ()</td></tr>
<tr class="memdesc:a3413eb0ad4f240f457f192f39e2e03e8 inherit pub_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Manually enable layer profiling times.  <a href="classtensorNet.html#a3413eb0ad4f240f457f192f39e2e03e8">More...</a><br /></td></tr>
<tr class="separator:a3413eb0ad4f240f457f192f39e2e03e8 inherit pub_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ae49f74ff83e46112a30318fa0576cace inherit pub_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#ae49f74ff83e46112a30318fa0576cace">EnableDebug</a> ()</td></tr>
<tr class="memdesc:ae49f74ff83e46112a30318fa0576cace inherit pub_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Manually enable debug messages and synchronization.  <a href="classtensorNet.html#ae49f74ff83e46112a30318fa0576cace">More...</a><br /></td></tr>
<tr class="separator:ae49f74ff83e46112a30318fa0576cace inherit pub_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a7d0ec0d8504ac8b26c5ab4a6136599ca inherit pub_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#a7d0ec0d8504ac8b26c5ab4a6136599ca">AllowGPUFallback</a> () const</td></tr>
<tr class="memdesc:a7d0ec0d8504ac8b26c5ab4a6136599ca inherit pub_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Return true if GPU fallback is enabled.  <a href="classtensorNet.html#a7d0ec0d8504ac8b26c5ab4a6136599ca">More...</a><br /></td></tr>
<tr class="separator:a7d0ec0d8504ac8b26c5ab4a6136599ca inherit pub_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a92bb737172d26bda5f67d15346a02514 inherit pub_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top"><a class="el" href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a>&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#a92bb737172d26bda5f67d15346a02514">GetDevice</a> () const</td></tr>
<tr class="memdesc:a92bb737172d26bda5f67d15346a02514 inherit pub_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the device being used for execution.  <a href="classtensorNet.html#a92bb737172d26bda5f67d15346a02514">More...</a><br /></td></tr>
<tr class="separator:a92bb737172d26bda5f67d15346a02514 inherit pub_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:afb38b5f171025e987a00214cc4379ca9 inherit pub_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top"><a class="el" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a>&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#afb38b5f171025e987a00214cc4379ca9">GetPrecision</a> () const</td></tr>
<tr class="memdesc:afb38b5f171025e987a00214cc4379ca9 inherit pub_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the type of precision being used.  <a href="classtensorNet.html#afb38b5f171025e987a00214cc4379ca9">More...</a><br /></td></tr>
<tr class="separator:afb38b5f171025e987a00214cc4379ca9 inherit pub_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a6b8e8dba05bc5c677027913d8c64f259 inherit pub_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#a6b8e8dba05bc5c677027913d8c64f259">IsPrecision</a> (<a class="el" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a> type) const</td></tr>
<tr class="memdesc:a6b8e8dba05bc5c677027913d8c64f259 inherit pub_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Check if a particular precision is being used.  <a href="classtensorNet.html#a6b8e8dba05bc5c677027913d8c64f259">More...</a><br /></td></tr>
<tr class="separator:a6b8e8dba05bc5c677027913d8c64f259 inherit pub_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a34e350ec6185277ac09ae55a79403e62 inherit pub_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">cudaStream_t&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#a34e350ec6185277ac09ae55a79403e62">GetStream</a> () const</td></tr>
<tr class="memdesc:a34e350ec6185277ac09ae55a79403e62 inherit pub_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the stream that the device is operating on.  <a href="classtensorNet.html#a34e350ec6185277ac09ae55a79403e62">More...</a><br /></td></tr>
<tr class="separator:a34e350ec6185277ac09ae55a79403e62 inherit pub_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a78cecfb7505be0ea59d29041abc85cbb inherit pub_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">cudaStream_t&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#a78cecfb7505be0ea59d29041abc85cbb">CreateStream</a> (bool nonBlocking=true)</td></tr>
<tr class="memdesc:a78cecfb7505be0ea59d29041abc85cbb inherit pub_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Create and use a new stream for execution.  <a href="classtensorNet.html#a78cecfb7505be0ea59d29041abc85cbb">More...</a><br /></td></tr>
<tr class="separator:a78cecfb7505be0ea59d29041abc85cbb inherit pub_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a679b177784c85bfdba63dcd1008ff633 inherit pub_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#a679b177784c85bfdba63dcd1008ff633">SetStream</a> (cudaStream_t stream)</td></tr>
<tr class="memdesc:a679b177784c85bfdba63dcd1008ff633 inherit pub_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Set the stream that the device is operating on.  <a href="classtensorNet.html#a679b177784c85bfdba63dcd1008ff633">More...</a><br /></td></tr>
<tr class="separator:a679b177784c85bfdba63dcd1008ff633 inherit pub_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a624881afe27acd2b2fff0f0f75308ea2 inherit pub_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">const char *&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#a624881afe27acd2b2fff0f0f75308ea2">GetPrototxtPath</a> () const</td></tr>
<tr class="memdesc:a624881afe27acd2b2fff0f0f75308ea2 inherit pub_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the path to the network prototxt file.  <a href="classtensorNet.html#a624881afe27acd2b2fff0f0f75308ea2">More...</a><br /></td></tr>
<tr class="separator:a624881afe27acd2b2fff0f0f75308ea2 inherit pub_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ac74d7f0571b7782b945ff85fd6894044 inherit pub_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">const char *&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#ac74d7f0571b7782b945ff85fd6894044">GetModelPath</a> () const</td></tr>
<tr class="memdesc:ac74d7f0571b7782b945ff85fd6894044 inherit pub_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the full path to model file, including the filename.  <a href="classtensorNet.html#ac74d7f0571b7782b945ff85fd6894044">More...</a><br /></td></tr>
<tr class="separator:ac74d7f0571b7782b945ff85fd6894044 inherit pub_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a03252bed041613fc1afb9d3cbb99663d inherit pub_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">const char *&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#a03252bed041613fc1afb9d3cbb99663d">GetModelFilename</a> () const</td></tr>
<tr class="memdesc:a03252bed041613fc1afb9d3cbb99663d inherit pub_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the filename of the file, excluding the directory.  <a href="classtensorNet.html#a03252bed041613fc1afb9d3cbb99663d">More...</a><br /></td></tr>
<tr class="separator:a03252bed041613fc1afb9d3cbb99663d inherit pub_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:acfa7f1f01b46f658ffc96f8a002e8d48 inherit pub_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top"><a class="el" href="group__tensorNet.html#ga5d4597e0e7beae7133d542e220528725">modelType</a>&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#acfa7f1f01b46f658ffc96f8a002e8d48">GetModelType</a> () const</td></tr>
<tr class="memdesc:acfa7f1f01b46f658ffc96f8a002e8d48 inherit pub_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the format of the network model.  <a href="classtensorNet.html#acfa7f1f01b46f658ffc96f8a002e8d48">More...</a><br /></td></tr>
<tr class="separator:acfa7f1f01b46f658ffc96f8a002e8d48 inherit pub_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a0a09d691ea080bd9734c5782c8fff6fd inherit pub_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#a0a09d691ea080bd9734c5782c8fff6fd">IsModelType</a> (<a class="el" href="group__tensorNet.html#ga5d4597e0e7beae7133d542e220528725">modelType</a> type) const</td></tr>
<tr class="memdesc:a0a09d691ea080bd9734c5782c8fff6fd inherit pub_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Return true if the model is of the specified format.  <a href="classtensorNet.html#a0a09d691ea080bd9734c5782c8fff6fd">More...</a><br /></td></tr>
<tr class="separator:a0a09d691ea080bd9734c5782c8fff6fd inherit pub_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ac583b8de1dd64b47338b4a3eb42ac166 inherit pub_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">uint32_t&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#ac583b8de1dd64b47338b4a3eb42ac166">GetInputLayers</a> () const</td></tr>
<tr class="memdesc:ac583b8de1dd64b47338b4a3eb42ac166 inherit pub_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the number of input layers to the network.  <a href="classtensorNet.html#ac583b8de1dd64b47338b4a3eb42ac166">More...</a><br /></td></tr>
<tr class="separator:ac583b8de1dd64b47338b4a3eb42ac166 inherit pub_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a2dcc770a7215e2e76a8d520a36689e16 inherit pub_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">uint32_t&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#a2dcc770a7215e2e76a8d520a36689e16">GetOutputLayers</a> () const</td></tr>
<tr class="memdesc:a2dcc770a7215e2e76a8d520a36689e16 inherit pub_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the number of output layers to the network.  <a href="classtensorNet.html#a2dcc770a7215e2e76a8d520a36689e16">More...</a><br /></td></tr>
<tr class="separator:a2dcc770a7215e2e76a8d520a36689e16 inherit pub_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:adcfe61596f291e75a87d36c3771f25df inherit pub_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top"><a class="el" href="tensorNet_8h.html#a64c8f3dfeacfa962ff9e23c586aedd1b">Dims3</a>&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#adcfe61596f291e75a87d36c3771f25df">GetInputDims</a> (uint32_t layer=0) const</td></tr>
<tr class="memdesc:adcfe61596f291e75a87d36c3771f25df inherit pub_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the dimensions of network input layer.  <a href="classtensorNet.html#adcfe61596f291e75a87d36c3771f25df">More...</a><br /></td></tr>
<tr class="separator:adcfe61596f291e75a87d36c3771f25df inherit pub_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a2d75ef6f579d1a71ff472bfafd0b7795 inherit pub_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">uint32_t&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#a2d75ef6f579d1a71ff472bfafd0b7795">GetInputWidth</a> (uint32_t layer=0) const</td></tr>
<tr class="memdesc:a2d75ef6f579d1a71ff472bfafd0b7795 inherit pub_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the width of network input layer.  <a href="classtensorNet.html#a2d75ef6f579d1a71ff472bfafd0b7795">More...</a><br /></td></tr>
<tr class="separator:a2d75ef6f579d1a71ff472bfafd0b7795 inherit pub_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a214a92c41dcdcb58b3cd8496aac0857a inherit pub_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">uint32_t&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#a214a92c41dcdcb58b3cd8496aac0857a">GetInputHeight</a> (uint32_t layer=0) const</td></tr>
<tr class="memdesc:a214a92c41dcdcb58b3cd8496aac0857a inherit pub_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the height of network input layer.  <a href="classtensorNet.html#a214a92c41dcdcb58b3cd8496aac0857a">More...</a><br /></td></tr>
<tr class="separator:a214a92c41dcdcb58b3cd8496aac0857a inherit pub_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a2c80d46f8a01335e77e41023544102c9 inherit pub_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">uint32_t&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#a2c80d46f8a01335e77e41023544102c9">GetInputSize</a> (uint32_t layer=0) const</td></tr>
<tr class="memdesc:a2c80d46f8a01335e77e41023544102c9 inherit pub_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the size (in bytes) of network input layer.  <a href="classtensorNet.html#a2c80d46f8a01335e77e41023544102c9">More...</a><br /></td></tr>
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<tr class="memitem:a3a8851513971d11746231d217f57b69f inherit pub_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">float *&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#a3a8851513971d11746231d217f57b69f">GetInputPtr</a> (uint32_t layer=0) const</td></tr>
<tr class="memdesc:a3a8851513971d11746231d217f57b69f inherit pub_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get the CUDA pointer to the input layer's memory.  <a href="classtensorNet.html#a3a8851513971d11746231d217f57b69f">More...</a><br /></td></tr>
<tr class="separator:a3a8851513971d11746231d217f57b69f inherit pub_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a77703f2a7b59f836c93ae28811e22cb0 inherit pub_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top"><a class="el" href="tensorNet_8h.html#a64c8f3dfeacfa962ff9e23c586aedd1b">Dims3</a>&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#a77703f2a7b59f836c93ae28811e22cb0">GetOutputDims</a> (uint32_t layer=0) const</td></tr>
<tr class="memdesc:a77703f2a7b59f836c93ae28811e22cb0 inherit pub_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the dimensions of network output layer.  <a href="classtensorNet.html#a77703f2a7b59f836c93ae28811e22cb0">More...</a><br /></td></tr>
<tr class="separator:a77703f2a7b59f836c93ae28811e22cb0 inherit pub_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a09d63a8fd906c99f8158bf9460a83c02 inherit pub_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">uint32_t&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#a09d63a8fd906c99f8158bf9460a83c02">GetOutputWidth</a> (uint32_t layer=0) const</td></tr>
<tr class="memdesc:a09d63a8fd906c99f8158bf9460a83c02 inherit pub_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the width of network output layer.  <a href="classtensorNet.html#a09d63a8fd906c99f8158bf9460a83c02">More...</a><br /></td></tr>
<tr class="separator:a09d63a8fd906c99f8158bf9460a83c02 inherit pub_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a613679e8ee5315f3b5b16a39011ba76e inherit pub_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">uint32_t&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#a613679e8ee5315f3b5b16a39011ba76e">GetOutputHeight</a> (uint32_t layer=0) const</td></tr>
<tr class="memdesc:a613679e8ee5315f3b5b16a39011ba76e inherit pub_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the height of network output layer.  <a href="classtensorNet.html#a613679e8ee5315f3b5b16a39011ba76e">More...</a><br /></td></tr>
<tr class="separator:a613679e8ee5315f3b5b16a39011ba76e inherit pub_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ae1486438dcdbe0d7f5e88e5336a42efa inherit pub_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">uint32_t&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#ae1486438dcdbe0d7f5e88e5336a42efa">GetOutputSize</a> (uint32_t layer=0) const</td></tr>
<tr class="memdesc:ae1486438dcdbe0d7f5e88e5336a42efa inherit pub_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the size (in bytes) of network output layer.  <a href="classtensorNet.html#ae1486438dcdbe0d7f5e88e5336a42efa">More...</a><br /></td></tr>
<tr class="separator:ae1486438dcdbe0d7f5e88e5336a42efa inherit pub_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a2e5a4207d90828c31255846b11a431ea inherit pub_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">float *&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#a2e5a4207d90828c31255846b11a431ea">GetOutputPtr</a> (uint32_t layer=0) const</td></tr>
<tr class="memdesc:a2e5a4207d90828c31255846b11a431ea inherit pub_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get the CUDA pointer to the output memory.  <a href="classtensorNet.html#a2e5a4207d90828c31255846b11a431ea">More...</a><br /></td></tr>
<tr class="separator:a2e5a4207d90828c31255846b11a431ea inherit pub_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a9dd2db089176ae6878e9ea7dd8fd80c3 inherit pub_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">float&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#a9dd2db089176ae6878e9ea7dd8fd80c3">GetNetworkFPS</a> ()</td></tr>
<tr class="memdesc:a9dd2db089176ae6878e9ea7dd8fd80c3 inherit pub_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the network frames per second (FPS).  <a href="classtensorNet.html#a9dd2db089176ae6878e9ea7dd8fd80c3">More...</a><br /></td></tr>
<tr class="separator:a9dd2db089176ae6878e9ea7dd8fd80c3 inherit pub_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a49faef5920860345e503023b7c84423c inherit pub_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">float&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#a49faef5920860345e503023b7c84423c">GetNetworkTime</a> ()</td></tr>
<tr class="memdesc:a49faef5920860345e503023b7c84423c inherit pub_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the network runtime (in milliseconds).  <a href="classtensorNet.html#a49faef5920860345e503023b7c84423c">More...</a><br /></td></tr>
<tr class="separator:a49faef5920860345e503023b7c84423c inherit pub_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ade7badd98d5790b5a58863d56e61e041 inherit pub_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">const char *&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#ade7badd98d5790b5a58863d56e61e041">GetNetworkName</a> () const</td></tr>
<tr class="memdesc:ade7badd98d5790b5a58863d56e61e041 inherit pub_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the network name (it's filename).  <a href="classtensorNet.html#ade7badd98d5790b5a58863d56e61e041">More...</a><br /></td></tr>
<tr class="separator:ade7badd98d5790b5a58863d56e61e041 inherit pub_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ad266f93035a80dca80cd84d971e4f69b inherit pub_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">float2&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#ad266f93035a80dca80cd84d971e4f69b">GetProfilerTime</a> (<a class="el" href="group__tensorNet.html#gae34d45c0faa674ef4cc0fbfc8fae5809">profilerQuery</a> query)</td></tr>
<tr class="memdesc:ad266f93035a80dca80cd84d971e4f69b inherit pub_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the profiler runtime (in milliseconds).  <a href="classtensorNet.html#ad266f93035a80dca80cd84d971e4f69b">More...</a><br /></td></tr>
<tr class="separator:ad266f93035a80dca80cd84d971e4f69b inherit pub_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a27cf81b3fecf93d2e63a61220a54b393 inherit pub_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">float&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#a27cf81b3fecf93d2e63a61220a54b393">GetProfilerTime</a> (<a class="el" href="group__tensorNet.html#gae34d45c0faa674ef4cc0fbfc8fae5809">profilerQuery</a> query, <a class="el" href="group__tensorNet.html#gaaa4127ed22c7165a32d0474ebf97975e">profilerDevice</a> device)</td></tr>
<tr class="memdesc:a27cf81b3fecf93d2e63a61220a54b393 inherit pub_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the profiler runtime (in milliseconds).  <a href="classtensorNet.html#a27cf81b3fecf93d2e63a61220a54b393">More...</a><br /></td></tr>
<tr class="separator:a27cf81b3fecf93d2e63a61220a54b393 inherit pub_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:afc0f50abcf6ac71e96d51eba3ed53d4b inherit pub_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#afc0f50abcf6ac71e96d51eba3ed53d4b">PrintProfilerTimes</a> ()</td></tr>
<tr class="memdesc:afc0f50abcf6ac71e96d51eba3ed53d4b inherit pub_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Print the profiler times (in millseconds).  <a href="classtensorNet.html#afc0f50abcf6ac71e96d51eba3ed53d4b">More...</a><br /></td></tr>
<tr class="separator:afc0f50abcf6ac71e96d51eba3ed53d4b inherit pub_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-static-methods"></a>
Static Public Member Functions</h2></td></tr>
<tr class="memitem:a6fddcf6fa38d337dbf0c9c8d64fca767"><td class="memItemLeft" align="right" valign="top">static <a class="el" href="classimageNet.html">imageNet</a> *&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classimageNet.html#a6fddcf6fa38d337dbf0c9c8d64fca767">Create</a> (const char *network=&quot;googlenet&quot;, uint32_t maxBatchSize=<a class="el" href="group__tensorNet.html#ga5a46a965749d6118e01307fd4d4865c9">DEFAULT_MAX_BATCH_SIZE</a>, <a class="el" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a> precision=<a class="el" href="group__tensorNet.html#ggaac6604fd52c6e5db82877390e0378623a1d325738f49e8e4c424ff671624e66f9">TYPE_FASTEST</a>, <a class="el" href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a> device=<a class="el" href="group__tensorNet.html#ggaa5d3f9981cdbd91516c1474006a80fe4adc7f3f88455afa81458863e5b3092e4b">DEVICE_GPU</a>, bool allowGPUFallback=true)</td></tr>
<tr class="memdesc:a6fddcf6fa38d337dbf0c9c8d64fca767"><td class="mdescLeft">&#160;</td><td class="mdescRight">Load one of the following pre-trained models:  <a href="classimageNet.html#a6fddcf6fa38d337dbf0c9c8d64fca767">More...</a><br /></td></tr>
<tr class="separator:a6fddcf6fa38d337dbf0c9c8d64fca767"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a9da8ad51bde43449ea159607ee97bb43"><td class="memItemLeft" align="right" valign="top">static <a class="el" href="classimageNet.html">imageNet</a> *&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classimageNet.html#a9da8ad51bde43449ea159607ee97bb43">Create</a> (const char *prototxt_path, const char *model_path, const char *mean_binary, const char *class_labels, const char *input=<a class="el" href="group__imageNet.html#ga00bb3120ef3040793ad3ee25d2727f5b">IMAGENET_DEFAULT_INPUT</a>, const char *output=<a class="el" href="group__imageNet.html#ga74a585b96a1bd960b5201f6b69752fad">IMAGENET_DEFAULT_OUTPUT</a>, uint32_t maxBatchSize=<a class="el" href="group__tensorNet.html#ga5a46a965749d6118e01307fd4d4865c9">DEFAULT_MAX_BATCH_SIZE</a>, <a class="el" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a> precision=<a class="el" href="group__tensorNet.html#ggaac6604fd52c6e5db82877390e0378623a1d325738f49e8e4c424ff671624e66f9">TYPE_FASTEST</a>, <a class="el" href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a> device=<a class="el" href="group__tensorNet.html#ggaa5d3f9981cdbd91516c1474006a80fe4adc7f3f88455afa81458863e5b3092e4b">DEVICE_GPU</a>, bool allowGPUFallback=true)</td></tr>
<tr class="memdesc:a9da8ad51bde43449ea159607ee97bb43"><td class="mdescLeft">&#160;</td><td class="mdescRight">Load a new network instance.  <a href="classimageNet.html#a9da8ad51bde43449ea159607ee97bb43">More...</a><br /></td></tr>
<tr class="separator:a9da8ad51bde43449ea159607ee97bb43"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a84b96993a80be279067e0508f649e602"><td class="memItemLeft" align="right" valign="top">static <a class="el" href="classimageNet.html">imageNet</a> *&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classimageNet.html#a84b96993a80be279067e0508f649e602">Create</a> (int argc, char **argv)</td></tr>
<tr class="memdesc:a84b96993a80be279067e0508f649e602"><td class="mdescLeft">&#160;</td><td class="mdescRight">Load a new network instance by parsing the command line.  <a href="classimageNet.html#a84b96993a80be279067e0508f649e602">More...</a><br /></td></tr>
<tr class="separator:a84b96993a80be279067e0508f649e602"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a08023940920cb17ae4968b9b36332a6a"><td class="memItemLeft" align="right" valign="top">static <a class="el" href="classimageNet.html">imageNet</a> *&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classimageNet.html#a08023940920cb17ae4968b9b36332a6a">Create</a> (const <a class="el" href="classcommandLine.html">commandLine</a> &amp;cmdLine)</td></tr>
<tr class="memdesc:a08023940920cb17ae4968b9b36332a6a"><td class="mdescLeft">&#160;</td><td class="mdescRight">Load a new network instance by parsing the command line.  <a href="classimageNet.html#a08023940920cb17ae4968b9b36332a6a">More...</a><br /></td></tr>
<tr class="separator:a08023940920cb17ae4968b9b36332a6a"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a7629a888728ef94bf35e573a96ebe4bd"><td class="memItemLeft" align="right" valign="top">static const char *&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classimageNet.html#a7629a888728ef94bf35e573a96ebe4bd">Usage</a> ()</td></tr>
<tr class="memdesc:a7629a888728ef94bf35e573a96ebe4bd"><td class="mdescLeft">&#160;</td><td class="mdescRight">Usage string for command line arguments to <a class="el" href="classimageNet.html#a6fddcf6fa38d337dbf0c9c8d64fca767" title="Load one of the following pre-trained models:">Create()</a>  <a href="classimageNet.html#a7629a888728ef94bf35e573a96ebe4bd">More...</a><br /></td></tr>
<tr class="separator:a7629a888728ef94bf35e573a96ebe4bd"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="inherit_header pub_static_methods_classtensorNet"><td colspan="2" onclick="javascript:toggleInherit('pub_static_methods_classtensorNet')"><img src="closed.png" alt="-"/>&#160;Static Public Member Functions inherited from <a class="el" href="classtensorNet.html">tensorNet</a></td></tr>
<tr class="memitem:a57cacfea82e9329c2cf776837dd00aef inherit pub_static_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">static bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#a57cacfea82e9329c2cf776837dd00aef">LoadClassLabels</a> (const char *filename, std::vector&lt; std::string &gt; &amp;descriptions, int expectedClasses=-1)</td></tr>
<tr class="memdesc:a57cacfea82e9329c2cf776837dd00aef inherit pub_static_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Load class descriptions from a label file.  <a href="classtensorNet.html#a57cacfea82e9329c2cf776837dd00aef">More...</a><br /></td></tr>
<tr class="separator:a57cacfea82e9329c2cf776837dd00aef inherit pub_static_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:aa92022958d3a46655a5e2f2ed416e6b5 inherit pub_static_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">static bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#aa92022958d3a46655a5e2f2ed416e6b5">LoadClassLabels</a> (const char *filename, std::vector&lt; std::string &gt; &amp;descriptions, std::vector&lt; std::string &gt; &amp;synsets, int expectedClasses=-1)</td></tr>
<tr class="memdesc:aa92022958d3a46655a5e2f2ed416e6b5 inherit pub_static_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Load class descriptions and synset strings from a label file.  <a href="classtensorNet.html#aa92022958d3a46655a5e2f2ed416e6b5">More...</a><br /></td></tr>
<tr class="separator:aa92022958d3a46655a5e2f2ed416e6b5 inherit pub_static_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a7b87410f9133aea37b46979d543219b9 inherit pub_static_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">static bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#a7b87410f9133aea37b46979d543219b9">LoadClassColors</a> (const char *filename, float4 *colors, int expectedClasses, float defaultAlpha=255.0f)</td></tr>
<tr class="memdesc:a7b87410f9133aea37b46979d543219b9 inherit pub_static_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Load class colors from a text file.  <a href="classtensorNet.html#a7b87410f9133aea37b46979d543219b9">More...</a><br /></td></tr>
<tr class="separator:a7b87410f9133aea37b46979d543219b9 inherit pub_static_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ae5dd58e2481f6c703abb9abbcfce805e inherit pub_static_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">static bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#ae5dd58e2481f6c703abb9abbcfce805e">LoadClassColors</a> (const char *filename, float4 **colors, int expectedClasses, float defaultAlpha=255.0f)</td></tr>
<tr class="memdesc:ae5dd58e2481f6c703abb9abbcfce805e inherit pub_static_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Load class colors from a text file.  <a href="classtensorNet.html#ae5dd58e2481f6c703abb9abbcfce805e">More...</a><br /></td></tr>
<tr class="separator:ae5dd58e2481f6c703abb9abbcfce805e inherit pub_static_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a4fe18908c74efda1708029ca3b04f0e8 inherit pub_static_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">static float4&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#a4fe18908c74efda1708029ca3b04f0e8">GenerateColor</a> (uint32_t <a class="el" href="cudaPointCloud_8h.html#ad9bd89745d72dbc52651f62814eed36d">classID</a>, float <a class="el" href="cudaVector_8h.html#ac0d98a665e25ffa6d701a2ce2f6efd12">alpha</a>=255.0f)</td></tr>
<tr class="memdesc:a4fe18908c74efda1708029ca3b04f0e8 inherit pub_static_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Procedurally generate a color for a given class index with the specified alpha value.  <a href="classtensorNet.html#a4fe18908c74efda1708029ca3b04f0e8">More...</a><br /></td></tr>
<tr class="separator:a4fe18908c74efda1708029ca3b04f0e8 inherit pub_static_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a3c0509631176be6f9e25673cb0aa12dc inherit pub_static_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">static <a class="el" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a>&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#a3c0509631176be6f9e25673cb0aa12dc">SelectPrecision</a> (<a class="el" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a> precision, <a class="el" href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a> device=<a class="el" href="group__tensorNet.html#ggaa5d3f9981cdbd91516c1474006a80fe4adc7f3f88455afa81458863e5b3092e4b">DEVICE_GPU</a>, bool allowInt8=true)</td></tr>
<tr class="memdesc:a3c0509631176be6f9e25673cb0aa12dc inherit pub_static_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Resolve a desired precision to a specific one that's available.  <a href="classtensorNet.html#a3c0509631176be6f9e25673cb0aa12dc">More...</a><br /></td></tr>
<tr class="separator:a3c0509631176be6f9e25673cb0aa12dc inherit pub_static_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:abe33fae5332296e2d917cb4ce435e255 inherit pub_static_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">static <a class="el" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a>&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#abe33fae5332296e2d917cb4ce435e255">FindFastestPrecision</a> (<a class="el" href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a> device=<a class="el" href="group__tensorNet.html#ggaa5d3f9981cdbd91516c1474006a80fe4adc7f3f88455afa81458863e5b3092e4b">DEVICE_GPU</a>, bool allowInt8=true)</td></tr>
<tr class="memdesc:abe33fae5332296e2d917cb4ce435e255 inherit pub_static_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Determine the fastest native precision on a device.  <a href="classtensorNet.html#abe33fae5332296e2d917cb4ce435e255">More...</a><br /></td></tr>
<tr class="separator:abe33fae5332296e2d917cb4ce435e255 inherit pub_static_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ae88436e652afdd7bceef7cb7c5fde7a6 inherit pub_static_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">static std::vector&lt; <a class="el" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a> &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#ae88436e652afdd7bceef7cb7c5fde7a6">DetectNativePrecisions</a> (<a class="el" href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a> device=<a class="el" href="group__tensorNet.html#ggaa5d3f9981cdbd91516c1474006a80fe4adc7f3f88455afa81458863e5b3092e4b">DEVICE_GPU</a>)</td></tr>
<tr class="memdesc:ae88436e652afdd7bceef7cb7c5fde7a6 inherit pub_static_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Detect the precisions supported natively on a device.  <a href="classtensorNet.html#ae88436e652afdd7bceef7cb7c5fde7a6">More...</a><br /></td></tr>
<tr class="separator:ae88436e652afdd7bceef7cb7c5fde7a6 inherit pub_static_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:aa3bf1a3bf1fca38b39a200b4d8f727b2 inherit pub_static_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">static bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#aa3bf1a3bf1fca38b39a200b4d8f727b2">DetectNativePrecision</a> (const std::vector&lt; <a class="el" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a> &gt; &amp;nativeTypes, <a class="el" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a> type)</td></tr>
<tr class="memdesc:aa3bf1a3bf1fca38b39a200b4d8f727b2 inherit pub_static_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Detect if a particular precision is supported natively.  <a href="classtensorNet.html#aa3bf1a3bf1fca38b39a200b4d8f727b2">More...</a><br /></td></tr>
<tr class="separator:aa3bf1a3bf1fca38b39a200b4d8f727b2 inherit pub_static_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a7d72ec8bbaf61278ce533afd60d5391c inherit pub_static_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">static bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#a7d72ec8bbaf61278ce533afd60d5391c">DetectNativePrecision</a> (<a class="el" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a> precision, <a class="el" href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a> device=<a class="el" href="group__tensorNet.html#ggaa5d3f9981cdbd91516c1474006a80fe4adc7f3f88455afa81458863e5b3092e4b">DEVICE_GPU</a>)</td></tr>
<tr class="memdesc:a7d72ec8bbaf61278ce533afd60d5391c inherit pub_static_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Detect if a particular precision is supported natively.  <a href="classtensorNet.html#a7d72ec8bbaf61278ce533afd60d5391c">More...</a><br /></td></tr>
<tr class="separator:a7d72ec8bbaf61278ce533afd60d5391c inherit pub_static_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pro-methods"></a>
Protected Member Functions</h2></td></tr>
<tr class="memitem:a0ea17be1ce78b3e0758af46c970a968c"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classimageNet.html#a0ea17be1ce78b3e0758af46c970a968c">imageNet</a> ()</td></tr>
<tr class="separator:a0ea17be1ce78b3e0758af46c970a968c"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:aa5321e8082e2dc35f5982882fa284181"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classimageNet.html#aa5321e8082e2dc35f5982882fa284181">init</a> (const char *prototxt_path, const char *model_path, const char *mean_binary, const char *class_path, const char *input, const char *output, uint32_t maxBatchSize, <a class="el" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a> precision, <a class="el" href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a> device, bool allowGPUFallback)</td></tr>
<tr class="separator:aa5321e8082e2dc35f5982882fa284181"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a6beef2c8d0972eaadad37abc89e74f95"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classimageNet.html#a6beef2c8d0972eaadad37abc89e74f95">loadClassInfo</a> (const char *filename, int expectedClasses=-1)</td></tr>
<tr class="separator:a6beef2c8d0972eaadad37abc89e74f95"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:abb118d7cf3f394a4e2d934c2c100fd1a"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classimageNet.html#abb118d7cf3f394a4e2d934c2c100fd1a">preProcess</a> (void *image, uint32_t width, uint32_t height, <a class="el" href="group__imageFormat.html#ga931c48e08f361637d093355d64583406">imageFormat</a> format)</td></tr>
<tr class="separator:abb118d7cf3f394a4e2d934c2c100fd1a"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a7e37e2320f4e40263a8169a4ee8d3280"><td class="memItemLeft" align="right" valign="top">float *&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classimageNet.html#a7e37e2320f4e40263a8169a4ee8d3280">applySmoothing</a> ()</td></tr>
<tr class="separator:a7e37e2320f4e40263a8169a4ee8d3280"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="inherit_header pro_methods_classtensorNet"><td colspan="2" onclick="javascript:toggleInherit('pro_methods_classtensorNet')"><img src="closed.png" alt="-"/>&#160;Protected Member Functions inherited from <a class="el" href="classtensorNet.html">tensorNet</a></td></tr>
<tr class="memitem:ab6e617d96e5542bef023ee9d4c96388a inherit pro_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#ab6e617d96e5542bef023ee9d4c96388a">tensorNet</a> ()</td></tr>
<tr class="memdesc:ab6e617d96e5542bef023ee9d4c96388a inherit pro_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Constructor.  <a href="classtensorNet.html#ab6e617d96e5542bef023ee9d4c96388a">More...</a><br /></td></tr>
<tr class="separator:ab6e617d96e5542bef023ee9d4c96388a inherit pro_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a2e8dd909e797dfcfbb058dc6b351c586 inherit pro_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#a2e8dd909e797dfcfbb058dc6b351c586">ProcessNetwork</a> (bool sync=true)</td></tr>
<tr class="memdesc:a2e8dd909e797dfcfbb058dc6b351c586 inherit pro_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Execute processing of the network.  <a href="classtensorNet.html#a2e8dd909e797dfcfbb058dc6b351c586">More...</a><br /></td></tr>
<tr class="separator:a2e8dd909e797dfcfbb058dc6b351c586 inherit pro_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a2fbc013f70b52f885867302446e0dca1 inherit pro_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#a2fbc013f70b52f885867302446e0dca1">ProfileModel</a> (const std::string &amp;deployFile, const std::string &amp;modelFile, const std::vector&lt; std::string &gt; &amp;inputs, const std::vector&lt; <a class="el" href="tensorNet_8h.html#a64c8f3dfeacfa962ff9e23c586aedd1b">Dims3</a> &gt; &amp;inputDims, const std::vector&lt; std::string &gt; &amp;outputs, uint32_t maxBatchSize, <a class="el" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a> precision, <a class="el" href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a> device, bool allowGPUFallback, nvinfer1::IInt8Calibrator *calibrator, char **engineStream, size_t *engineSize)</td></tr>
<tr class="memdesc:a2fbc013f70b52f885867302446e0dca1 inherit pro_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Create and output an optimized network model.  <a href="classtensorNet.html#a2fbc013f70b52f885867302446e0dca1">More...</a><br /></td></tr>
<tr class="separator:a2fbc013f70b52f885867302446e0dca1 inherit pro_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a7a898dfb2553869cdc318ecb03e153f1 inherit pro_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#a7a898dfb2553869cdc318ecb03e153f1">ConfigureBuilder</a> (nvinfer1::IBuilder *builder, uint32_t maxBatchSize, uint32_t workspaceSize, <a class="el" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a> precision, <a class="el" href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a> device, bool allowGPUFallback, nvinfer1::IInt8Calibrator *calibrator)</td></tr>
<tr class="memdesc:a7a898dfb2553869cdc318ecb03e153f1 inherit pro_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Configure builder options.  <a href="classtensorNet.html#a7a898dfb2553869cdc318ecb03e153f1">More...</a><br /></td></tr>
<tr class="separator:a7a898dfb2553869cdc318ecb03e153f1 inherit pro_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a6e2fe0a467929d76b20940771b8f96c3 inherit pro_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#a6e2fe0a467929d76b20940771b8f96c3">ValidateEngine</a> (const char *model_path, const char *cache_path, const char *checksum_path)</td></tr>
<tr class="memdesc:a6e2fe0a467929d76b20940771b8f96c3 inherit pro_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Validate that the model already has a built TensorRT engine that exists and doesn't need updating.  <a href="classtensorNet.html#a6e2fe0a467929d76b20940771b8f96c3">More...</a><br /></td></tr>
<tr class="separator:a6e2fe0a467929d76b20940771b8f96c3 inherit pro_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a088c3bf591e45e52ec227491f6f299ad inherit pro_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#a088c3bf591e45e52ec227491f6f299ad">PROFILER_BEGIN</a> (<a class="el" href="group__tensorNet.html#gae34d45c0faa674ef4cc0fbfc8fae5809">profilerQuery</a> query)</td></tr>
<tr class="memdesc:a088c3bf591e45e52ec227491f6f299ad inherit pro_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Begin a profiling query, before network is run.  <a href="classtensorNet.html#a088c3bf591e45e52ec227491f6f299ad">More...</a><br /></td></tr>
<tr class="separator:a088c3bf591e45e52ec227491f6f299ad inherit pro_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ac8582b9a6099e3265da4c3f9fdf804ea inherit pro_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#ac8582b9a6099e3265da4c3f9fdf804ea">PROFILER_END</a> (<a class="el" href="group__tensorNet.html#gae34d45c0faa674ef4cc0fbfc8fae5809">profilerQuery</a> query)</td></tr>
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<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pro-attribs"></a>
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<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><p>Image recognition with classification networks, using TensorRT. </p>
</div><h2 class="groupheader">Member Typedef Documentation</h2>
<a id="aae203e533ecceb314857f99a2817fc81"></a>
<h2 class="memtitle"><span class="permalink"><a href="#aae203e533ecceb314857f99a2817fc81">&#9670;&nbsp;</a></span>Classifications</h2>

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          <td class="memname">typedef std::vector&lt;std::pair&lt;uint32_t, float&gt; &gt; <a class="el" href="classimageNet.html#aae203e533ecceb314857f99a2817fc81">imageNet::Classifications</a></td>
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<p>List of classification results where each entry represents a (classID, confidence) pair. </p>

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</div>
<h2 class="groupheader">Constructor &amp; Destructor Documentation</h2>
<a id="af6bd86e81ff9e67ffe19b575c17ed104"></a>
<h2 class="memtitle"><span class="permalink"><a href="#af6bd86e81ff9e67ffe19b575c17ed104">&#9670;&nbsp;</a></span>~imageNet()</h2>

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          <td class="memname">virtual imageNet::~imageNet </td>
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<p>Destroy. </p>

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<h2 class="memtitle"><span class="permalink"><a href="#a0ea17be1ce78b3e0758af46c970a968c">&#9670;&nbsp;</a></span>imageNet()</h2>

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          <td class="memname">imageNet::imageNet </td>
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<h2 class="groupheader">Member Function Documentation</h2>
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<h2 class="memtitle"><span class="permalink"><a href="#a7e37e2320f4e40263a8169a4ee8d3280">&#9670;&nbsp;</a></span>applySmoothing()</h2>

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          <td class="memname">float* imageNet::applySmoothing </td>
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<h2 class="memtitle"><span class="permalink"><a href="#a1c645a989562b8bc8f1137f0f43fc6b7">&#9670;&nbsp;</a></span>Classify() <span class="overload">[1/5]</span></h2>

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          <td class="memname">int imageNet::Classify </td>
          <td>(</td>
          <td class="paramtype">float *&#160;</td>
          <td class="paramname"><em>rgba</em>, </td>
        </tr>
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          <td class="paramkey"></td>
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          <td class="paramtype">uint32_t&#160;</td>
          <td class="paramname"><em>width</em>, </td>
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          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">uint32_t&#160;</td>
          <td class="paramname"><em>height</em>, </td>
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          <td class="paramkey"></td>
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          <td class="paramtype">float *&#160;</td>
          <td class="paramname"><em>confidence</em> = <code>NULL</code>, </td>
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          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype"><a class="el" href="group__imageFormat.html#ga931c48e08f361637d093355d64583406">imageFormat</a>&#160;</td>
          <td class="paramname"><em>format</em> = <code><a class="el" href="group__imageFormat.html#gga931c48e08f361637d093355d64583406a9396c3fdae6987bbf4abc2b2e63e3815">IMAGE_RGBA32F</a></code>&#160;</td>
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<p>Predict the maximum-likelihood image class whose confidence meets the minimum threshold. </p>
<p>Either the class with the maximum probability will be returned, or -1 if no class meets the threshold set by <a class="el" href="classimageNet.html#a6594a1190c5d515c1987aefcee5d819f" title="Set the confidence threshold used for classification.">SetThreshold()</a> or the <code>--threshold</code> command-line argument.</p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">rgba</td><td>float4 input image in CUDA device memory. </td></tr>
    <tr><td class="paramname">width</td><td>width of the input image in pixels. </td></tr>
    <tr><td class="paramname">height</td><td>height of the input image in pixels. </td></tr>
    <tr><td class="paramname">confidence</td><td>optional pointer to float filled with confidence value. </td></tr>
    <tr><td class="paramname">format</td><td>format of the image (rgb8, rgba8, rgb32f, rgba32f are supported)</td></tr>
  </table>
  </dd>
</dl>
<dl class="section return"><dt>Returns</dt><dd>ID of the class with the highest confidence, or -1 if no classes met the threshold. If a runtime error occurred during processing, then a value of -2 will be returned. </dd></dl>
<dl class="deprecated"><dt><b><a class="el" href="deprecated.html#_deprecated000007">Deprecated:</a></b></dt><dd>this overload of <a class="el" href="classimageNet.html#a0023fb5bcd45dcefdac6a7df4bf9bab5" title="Predict the maximum-likelihood image class whose confidence meets the minimum threshold.">Classify()</a> provides legacy compatibility with <code>float*</code> type (RGBA32F).</dd></dl>

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<h2 class="memtitle"><span class="permalink"><a href="#ad39000debfd9ac6352f85b93ae27b305">&#9670;&nbsp;</a></span>Classify() <span class="overload">[2/5]</span></h2>

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          <td class="memname">int imageNet::Classify </td>
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          <td class="paramtype">T *&#160;</td>
          <td class="paramname"><em>image</em>, </td>
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          <td class="paramname"><em>width</em>, </td>
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          <td class="paramtype">uint32_t&#160;</td>
          <td class="paramname"><em>height</em>, </td>
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          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype"><a class="el" href="classimageNet.html#aae203e533ecceb314857f99a2817fc81">Classifications</a> &amp;&#160;</td>
          <td class="paramname"><em>classifications</em>, </td>
        </tr>
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          <td class="paramkey"></td>
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          <td class="paramtype">int&#160;</td>
          <td class="paramname"><em>topK</em> = <code>1</code>&#160;</td>
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<p>Classify the image and return the topK image classification results that meet the minimum confidence threshold set by <a class="el" href="classimageNet.html#a6594a1190c5d515c1987aefcee5d819f" title="Set the confidence threshold used for classification.">SetThreshold()</a> or the <code>--threshold</code> command-line argument. </p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">image</td><td>input image in CUDA device memory. </td></tr>
    <tr><td class="paramname">width</td><td>width of the input image in pixels. </td></tr>
    <tr><td class="paramname">height</td><td>height of the input image in pixels. </td></tr>
    <tr><td class="paramname">classifications</td><td>returns a list of the topK (classID, confidence) classification resuts, sorted from highest to lowest confidence. </td></tr>
    <tr><td class="paramname">topK</td><td>the number of predictions to return (it can be less than this number if there weren't that many valid predictions) The default value of topK is 1, in which case only the highest-confidence result wil be returned. If the value of topK is &lt;= 0, then all the valid predictions with confidence &gt;= threshold will be returned.</td></tr>
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  </dd>
</dl>
<dl class="section return"><dt>Returns</dt><dd>ID of the class with the highest confidence, or -1 if no classes met the threshold. If a runtime error occurred during processing, then a value of -2 will be returned. </dd></dl>

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<h2 class="memtitle"><span class="permalink"><a href="#a0023fb5bcd45dcefdac6a7df4bf9bab5">&#9670;&nbsp;</a></span>Classify() <span class="overload">[3/5]</span></h2>

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          <td class="paramtype">T *&#160;</td>
          <td class="paramname"><em>image</em>, </td>
        </tr>
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          <td class="paramkey"></td>
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          <td class="paramname"><em>width</em>, </td>
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          <td class="paramtype">uint32_t&#160;</td>
          <td class="paramname"><em>height</em>, </td>
        </tr>
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          <td class="paramkey"></td>
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          <td class="paramtype">float *&#160;</td>
          <td class="paramname"><em>confidence</em> = <code>NULL</code>&#160;</td>
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<p>Predict the maximum-likelihood image class whose confidence meets the minimum threshold. </p>
<p>Either the class with the maximum probability will be returned, or -1 if no class meets the threshold set by <a class="el" href="classimageNet.html#a6594a1190c5d515c1987aefcee5d819f" title="Set the confidence threshold used for classification.">SetThreshold()</a> or the <code>--threshold</code> command-line argument.</p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">image</td><td>input image in CUDA device memory. </td></tr>
    <tr><td class="paramname">width</td><td>width of the input image in pixels. </td></tr>
    <tr><td class="paramname">height</td><td>height of the input image in pixels. </td></tr>
    <tr><td class="paramname">confidence</td><td>optional pointer to float filled with confidence value.</td></tr>
  </table>
  </dd>
</dl>
<dl class="section return"><dt>Returns</dt><dd>ID of the class with the highest confidence, or -1 if no classes met the threshold. If a runtime error occurred during processing, then a value of -2 will be returned. </dd></dl>

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<h2 class="memtitle"><span class="permalink"><a href="#a364792017fbc14e0464f72f4e2bc1267">&#9670;&nbsp;</a></span>Classify() <span class="overload">[4/5]</span></h2>

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          <td class="memname">int imageNet::Classify </td>
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          <td class="paramtype">void *&#160;</td>
          <td class="paramname"><em>image</em>, </td>
        </tr>
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          <td class="paramkey"></td>
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          <td class="paramname"><em>width</em>, </td>
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          <td class="paramtype">uint32_t&#160;</td>
          <td class="paramname"><em>height</em>, </td>
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          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype"><a class="el" href="group__imageFormat.html#ga931c48e08f361637d093355d64583406">imageFormat</a>&#160;</td>
          <td class="paramname"><em>format</em>, </td>
        </tr>
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          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype"><a class="el" href="classimageNet.html#aae203e533ecceb314857f99a2817fc81">Classifications</a> &amp;&#160;</td>
          <td class="paramname"><em>classifications</em>, </td>
        </tr>
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          <td class="paramtype">int&#160;</td>
          <td class="paramname"><em>topK</em> = <code>1</code>&#160;</td>
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<p>Classify the image and return the topK image classification results that meet the minimum confidence threshold set by <a class="el" href="classimageNet.html#a6594a1190c5d515c1987aefcee5d819f" title="Set the confidence threshold used for classification.">SetThreshold()</a> or the <code>--threshold</code> command-line argument. </p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">image</td><td>input image in CUDA device memory. </td></tr>
    <tr><td class="paramname">width</td><td>width of the input image in pixels. </td></tr>
    <tr><td class="paramname">height</td><td>height of the input image in pixels. </td></tr>
    <tr><td class="paramname">format</td><td>format of the image (rgb8, rgba8, rgb32f, rgba32f are supported) </td></tr>
    <tr><td class="paramname">classifications</td><td>returns a list of the topK (classID, confidence) classification resuts, sorted from highest to lowest confidence. </td></tr>
    <tr><td class="paramname">topK</td><td>the number of predictions to return (it can be less than this number if there weren't that many valid predictions) The default value of topK is 1, in which case only the highest-confidence result wil be returned. If the value of topK is &lt;= 0, then all the valid predictions with confidence &gt;= threshold will be returned.</td></tr>
  </table>
  </dd>
</dl>
<dl class="section return"><dt>Returns</dt><dd>ID of the class with the highest confidence, or -1 if no classes met the threshold. If a runtime error occurred during processing, then a value of -2 will be returned. </dd></dl>

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<h2 class="memtitle"><span class="permalink"><a href="#adfaf6892bc745af042089ee995b33745">&#9670;&nbsp;</a></span>Classify() <span class="overload">[5/5]</span></h2>

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          <td class="memname">int imageNet::Classify </td>
          <td>(</td>
          <td class="paramtype">void *&#160;</td>
          <td class="paramname"><em>image</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">uint32_t&#160;</td>
          <td class="paramname"><em>width</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">uint32_t&#160;</td>
          <td class="paramname"><em>height</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype"><a class="el" href="group__imageFormat.html#ga931c48e08f361637d093355d64583406">imageFormat</a>&#160;</td>
          <td class="paramname"><em>format</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">float *&#160;</td>
          <td class="paramname"><em>confidence</em> = <code>NULL</code>&#160;</td>
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<p>Predict the maximum-likelihood image class whose confidence meets the minimum threshold. </p>
<p>Either the class with the maximum probability will be returned, or -1 if no class meets the threshold set by <a class="el" href="classimageNet.html#a6594a1190c5d515c1987aefcee5d819f" title="Set the confidence threshold used for classification.">SetThreshold()</a> or the <code>--threshold</code> command-line argument.</p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">image</td><td>input image in CUDA device memory. </td></tr>
    <tr><td class="paramname">width</td><td>width of the input image in pixels. </td></tr>
    <tr><td class="paramname">height</td><td>height of the input image in pixels. </td></tr>
    <tr><td class="paramname">format</td><td>format of the image (rgb8, rgba8, rgb32f, rgba32f are supported) </td></tr>
    <tr><td class="paramname">confidence</td><td>optional pointer to float filled with confidence value.</td></tr>
  </table>
  </dd>
</dl>
<dl class="section return"><dt>Returns</dt><dd>ID of the class with the highest confidence, or -1 if no classes met the threshold. If a runtime error occurred during processing, then a value of -2 will be returned. </dd></dl>

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<h2 class="memtitle"><span class="permalink"><a href="#a6fddcf6fa38d337dbf0c9c8d64fca767">&#9670;&nbsp;</a></span>Create() <span class="overload">[1/4]</span></h2>

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          <td>(</td>
          <td class="paramtype">const char *&#160;</td>
          <td class="paramname"><em>network</em> = <code>&quot;googlenet&quot;</code>, </td>
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          <td></td>
          <td class="paramtype">uint32_t&#160;</td>
          <td class="paramname"><em>maxBatchSize</em> = <code><a class="el" href="group__tensorNet.html#ga5a46a965749d6118e01307fd4d4865c9">DEFAULT_MAX_BATCH_SIZE</a></code>, </td>
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          <td class="paramtype"><a class="el" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a>&#160;</td>
          <td class="paramname"><em>precision</em> = <code><a class="el" href="group__tensorNet.html#ggaac6604fd52c6e5db82877390e0378623a1d325738f49e8e4c424ff671624e66f9">TYPE_FASTEST</a></code>, </td>
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          <td class="paramkey"></td>
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          <td class="paramtype"><a class="el" href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a>&#160;</td>
          <td class="paramname"><em>device</em> = <code><a class="el" href="group__tensorNet.html#ggaa5d3f9981cdbd91516c1474006a80fe4adc7f3f88455afa81458863e5b3092e4b">DEVICE_GPU</a></code>, </td>
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          <td class="paramkey"></td>
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          <td class="paramtype">bool&#160;</td>
          <td class="paramname"><em>allowGPUFallback</em> = <code>true</code>&#160;</td>
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          <td>)</td>
          <td></td><td></td>
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<p>Load one of the following pre-trained models: </p>
<ul>
<li>alexnet, googlenet, googlenet-12,</li>
<li>resnet-18, resnet-50, resnet-101, resnet-152,</li>
<li>vgg-16, vgg-19, inception-v4</li>
</ul>
<p>These are all 1000-class models trained on ImageNet ILSVRC, except for googlenet-12 which is a 12-class subset of ILSVRC. </p>

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          <td>(</td>
          <td class="paramtype">const char *&#160;</td>
          <td class="paramname"><em>prototxt_path</em>, </td>
        </tr>
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          <td class="paramname"><em>model_path</em>, </td>
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          <td class="paramtype">const char *&#160;</td>
          <td class="paramname"><em>mean_binary</em>, </td>
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          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const char *&#160;</td>
          <td class="paramname"><em>class_labels</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const char *&#160;</td>
          <td class="paramname"><em>input</em> = <code><a class="el" href="group__imageNet.html#ga00bb3120ef3040793ad3ee25d2727f5b">IMAGENET_DEFAULT_INPUT</a></code>, </td>
        </tr>
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          <td class="paramtype">const char *&#160;</td>
          <td class="paramname"><em>output</em> = <code><a class="el" href="group__imageNet.html#ga74a585b96a1bd960b5201f6b69752fad">IMAGENET_DEFAULT_OUTPUT</a></code>, </td>
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          <td class="paramtype">uint32_t&#160;</td>
          <td class="paramname"><em>maxBatchSize</em> = <code><a class="el" href="group__tensorNet.html#ga5a46a965749d6118e01307fd4d4865c9">DEFAULT_MAX_BATCH_SIZE</a></code>, </td>
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<p>Load a new network instance. </p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">prototxt_path</td><td>File path to the deployable network prototxt </td></tr>
    <tr><td class="paramname">model_path</td><td>File path to the caffemodel </td></tr>
    <tr><td class="paramname">mean_binary</td><td>File path to the mean value binary proto (can be NULL) </td></tr>
    <tr><td class="paramname">class_labels</td><td>File path to list of class name labels </td></tr>
    <tr><td class="paramname">input</td><td>Name of the input layer blob. </td></tr>
    <tr><td class="paramname">output</td><td>Name of the output layer blob. </td></tr>
    <tr><td class="paramname">maxBatchSize</td><td>The maximum batch size that the network will support and be optimized for. </td></tr>
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<p>Load a new network instance by parsing the command line. </p>

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<p>Load a new network instance by parsing the command line. </p>

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<h2 class="memtitle"><span class="permalink"><a href="#a673728c04ae909cb3068c2a1ace1e5a7">&#9670;&nbsp;</a></span>GetClassDesc()</h2>

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<p>Retrieve the description of a particular class. </p>

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<h2 class="memtitle"><span class="permalink"><a href="#a3b41ed0e039638353e6964ada588becb">&#9670;&nbsp;</a></span>GetClassLabel()</h2>

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<p>Retrieve the description of a particular class. </p>

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<h2 class="memtitle"><span class="permalink"><a href="#a04276f915b0f40d6257cbed3fe47dc5f">&#9670;&nbsp;</a></span>GetClassPath()</h2>

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<p>Retrieve the path to the file containing the class descriptions. </p>

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<h2 class="memtitle"><span class="permalink"><a href="#ab8ee0abaa0ebf38becfc0cbaf6956712">&#9670;&nbsp;</a></span>GetClassSynset()</h2>

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<p>Retrieve the class synset category of a particular class. </p>

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<h2 class="memtitle"><span class="permalink"><a href="#a478f25126524a256e81ec264aad7e27a">&#9670;&nbsp;</a></span>GetNumClasses()</h2>

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<p>Retrieve the number of image recognition classes (typically 1000) </p>

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<h2 class="memtitle"><span class="permalink"><a href="#a37b850912da4c60f93c7798d13baf777">&#9670;&nbsp;</a></span>GetSmoothing()</h2>

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<p>Return the temporal smoothing weight or number of frames in the smoothing window. </p>
<dl class="section see"><dt>See also</dt><dd><a class="el" href="classimageNet.html#adc93ae6107469e3898872917f6736d85" title="Enable temporal smoothing of the results using EWMA (exponentially-weighted moving average).">SetSmoothing</a> </dd></dl>

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<h2 class="memtitle"><span class="permalink"><a href="#ac074f4e67f639c89238a3dcb6771c4cd">&#9670;&nbsp;</a></span>GetThreshold()</h2>

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<p>Return the confidence threshold used for classification. </p>

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<h2 class="memtitle"><span class="permalink"><a href="#aa5321e8082e2dc35f5982882fa284181">&#9670;&nbsp;</a></span>init()</h2>

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          <td class="paramname"><em>class_path</em>, </td>
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          <td class="paramtype">const char *&#160;</td>
          <td class="paramname"><em>input</em>, </td>
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          <td class="paramtype">const char *&#160;</td>
          <td class="paramname"><em>output</em>, </td>
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          <td class="paramtype">uint32_t&#160;</td>
          <td class="paramname"><em>maxBatchSize</em>, </td>
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          <td class="paramtype"><a class="el" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a>&#160;</td>
          <td class="paramname"><em>precision</em>, </td>
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          <td class="paramtype"><a class="el" href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a>&#160;</td>
          <td class="paramname"><em>device</em>, </td>
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          <td class="paramtype">bool&#160;</td>
          <td class="paramname"><em>allowGPUFallback</em>&#160;</td>
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<h2 class="memtitle"><span class="permalink"><a href="#a6beef2c8d0972eaadad37abc89e74f95">&#9670;&nbsp;</a></span>loadClassInfo()</h2>

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<h2 class="memtitle"><span class="permalink"><a href="#abb118d7cf3f394a4e2d934c2c100fd1a">&#9670;&nbsp;</a></span>preProcess()</h2>

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          <td class="paramname"><em>format</em>&#160;</td>
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<h2 class="memtitle"><span class="permalink"><a href="#adc93ae6107469e3898872917f6736d85">&#9670;&nbsp;</a></span>SetSmoothing()</h2>

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<p>Enable temporal smoothing of the results using EWMA (exponentially-weighted moving average). </p>
<p>This filters the confidence values of each class over ~N frames to reduce noise and jitter. In lieu of storing a history of past data, this uses an accumulated approximation of EMA:</p>
<p>EMA(x,t) = EMA(x, t-1) + w * (x - EMA(x, t-1))</p>
<p>where x is a class softmax output logit, t is the timestep, and w is the smoothing weight.</p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">factor</td><td>either a weight between [0,1] that's placed on the latest confidence values, or the smoothing window as a number of frames (where the weight will be 1/N). <br  />
 For example, a factor of N=5 would average over approximately the last 5 frames, and would be equivalent to specifying a weight of 0.2 (either can be used). A weight closer to 1 will be more responsive to changes, but also more noisy. <br  />
 Setting this to 0 or 1 will disable smoothing and use the unfiltered outputs.</td></tr>
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<dl class="section note"><dt>Note</dt><dd>this can also be set using the <code>--smoothing=N</code> command-line argument. </dd></dl>

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<h2 class="memtitle"><span class="permalink"><a href="#a6594a1190c5d515c1987aefcee5d819f">&#9670;&nbsp;</a></span>SetThreshold()</h2>

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<p>Set the confidence threshold used for classification. </p>
<p>Classes with a confidence below this threshold will be ignored. </p><dl class="section note"><dt>Note</dt><dd>this can also be set using the <code>--threshold=N</code> command-line argument. </dd></dl>

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<h2 class="memtitle"><span class="permalink"><a href="#a7629a888728ef94bf35e573a96ebe4bd">&#9670;&nbsp;</a></span>Usage()</h2>

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<p>Usage string for command line arguments to <a class="el" href="classimageNet.html#a6fddcf6fa38d337dbf0c9c8d64fca767" title="Load one of the following pre-trained models:">Create()</a> </p>

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<h2 class="memtitle"><span class="permalink"><a href="#a9c75cea83d0c3e605aef8c0dd8e43177">&#9670;&nbsp;</a></span>mClassDesc</h2>

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<h2 class="memtitle"><span class="permalink"><a href="#abd00b812a1f39a0bd23c43a8807d6193">&#9670;&nbsp;</a></span>mClassSynset</h2>

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<h2 class="memtitle"><span class="permalink"><a href="#ac41444447cc6a5caa2430af2a6633392">&#9670;&nbsp;</a></span>mSmoothingFactor</h2>

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<h2 class="memtitle"><span class="permalink"><a href="#a1db7d7ac6160c242b5388139d1bd8030">&#9670;&nbsp;</a></span>mThreshold</h2>

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<hr/>The documentation for this class was generated from the following file:<ul>
<li>jetson-inference/<a class="el" href="imageNet_8h_source.html">imageNet.h</a></li>
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